# Vector Database Engineer > Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar Use when this capability is needed. - Skill: `tomevault-io/vector-database-engineer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/vector-database-engineer` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/vector-database-engineer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/vector-database-engineer --- # Vector Database Engineer Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similarity search. Use PROACTIVELY for vector search implementation, embedding optimization, or semantic retrieval systems. ## Do not use this skill when - The task is unrelated to vector database engineer - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. ## Capabilities - Vector database selection and architecture - Embedding model selection and optimization - Index configuration (HNSW, IVF, PQ) - Hybrid search (vector + keyword) implementation - Chunking strategies for documents - Metadata filtering and pre/post-filtering - Performance tuning and scaling ## Use this skill when - Building RAG (Retrieval Augmented Generation) systems - Implementing semantic search over documents - Creating recommendation engines - Building image/audio similarity search - Optimizing vector search latency and recall - Scaling vector operations to millions of vectors ## Workflow 1. Analyze data characteristics and query patterns 2. Select appropriate embedding model 3. Design chunking and preprocessing pipeline 4. Choose vector database and index type 5. Configure metadata schema for filtering 6. Implement hybrid search if needed 7. Optimize for latency/recall tradeoffs 8. Set up monitoring and reindexing strategies ## Best Practices - Choose embedding dimensions based on use case (384-1536) - Implement proper chunking with overlap - Use metadata filtering to reduce search space - Monitor embedding drift over time - Plan for index rebuilding - Cache frequent queries - Test recall vs latency tradeoffs --- ## AGI Framework Integration > **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)** > Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills) ### Memory-First Protocol Retrieve prior API design decisions, database schema choices, and error handling patterns. Cache API response templates for consistent error formatting. ```bash # Check for prior backend/API context before starting python3 execution/memory_manager.py auto --query "API design patterns and architecture decisions for Vector Database Engineer" ``` ### Storing Results After completing work, store backend/API decisions for future sessions: ```bash python3 execution/memory_manager.py store \ --content "API architecture: REST with HATEOAS, JWT auth, rate limiting at 100 req/min per tenant" \ --type decision --project \ --tags vector-database-engineer backend ``` ### Multi-Agent Collaboration Share API contract changes with frontend agents so they update their client code, and with QA agents for test coverage. ```bash python3 execution/cross_agent_context.py store \ --agent "" \ --action "Implemented API endpoints — 5 new routes with OpenAPI spec and integration tests" \ --project ``` ### Agent Team: Code Review After implementation, dispatch `code_review_team` for two-stage review (spec compliance + code quality) before merging. --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/techwavedev) — claim your Tome and manage your conversions.